| 城市典型路段的驾驶员酒驾行为识别 |
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| 引用本文： | 李敏,王武宏,蒋晓蓓,陈涛,王丰元.城市典型路段的驾驶员酒驾行为识别[J].北京理工大学学报,2016,36(S2):185-188. |
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| 作者姓名： | 李敏 王武宏 蒋晓蓓 陈涛 王丰元 |
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| 作者单位： | 北京理工大学 机械与车辆学院, 北京 100081;青岛理工大学 汽车与交通学院, 山东, 青岛 266520,北京理工大学 机械与车辆学院, 北京 100081,北京理工大学 机械与车辆学院, 北京 100081,长安大学 汽车学院, 陕西, 西安 710064,青岛理工大学 汽车与交通学院, 山东, 青岛 266520 |
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| 基金项目： | 中央高校基本科研业务费专项资金资助项目（310822151119）；北京理工大学研究生科技创新活动专项计划项目（2015CX10011） |
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| 摘 要： | 目前对驾驶员进行是否酒驾的接触式、非实时的随机抽检方式已难以满足酒驾检测的实际需求.在加速行驶路段、匀速行驶路段、转弯路段等典型城市道路下，以车辆速度、加速度和油门踏板位置、发动机转速等交通参数为输入，采用支持向量机模型对驾驶员的驾驶行为进行识别并判定其是否处于酒驾状态，并采用粒子群优化算法对模型参数进行优化以提高训练速度.研究结果表明，基于粒子群优化算法的支持向量机模型能快速、准确地判定驾驶员是否处于酒驾状态，可为实现非接触式酒驾检测提供理论支持，为安全驾驶辅助系统采取相应措施提供实现基础.
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| 关 键 词： | 驾驶行为 酒驾 粒子群算法 支持向量机 |
| 收稿时间： | 2016/10/30 0:00:00 |
Identifying Drunk Driving Behavior on Urban Typical Road Section |
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| LI Min,WANG Wu-hong,JIANG Xiao-bei,CHENTao and WANG Feng-Yuan.Identifying Drunk Driving Behavior on Urban Typical Road Section[J].Journal of Beijing Institute of Technology(Natural Science Edition),2016,36(S2):185-188. |
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| Authors: | LI Min WANG Wu-hong JIANG Xiao-bei CHENTao and WANG Feng-Yuan |
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| Institution: | Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China;School of Automobile and Transportation, Qingdao University of Technology, Qingdao, Shandong 266520, China,Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China,Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China,Key Laboratory of Automotive Transportation Safety Techniques of Ministry of Transport, Chang''an University, Xi''an, Shaanxi 710064, China and School of Automobile and Transportation, Qingdao University of Technology, Qingdao, Shandong 266520, China |
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| Abstract: | The random, contacting and non-real-time test method of alcohol for drivers cannot satisfy the current situations now. Under the typical traffic conditions of urban road during accelerating, speed-keeping and turning section,vehicle speed, acceleration, position of gas pedal, and revolving speed of engine were taken as input variables.And a Support Vector Machine (SVM) model was introduced to identify the drivers'' behaviors and to estimate whether the driver was under alcohol influence. To improve the training efficiencyof the model, Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters selection progress. The result proves that the SVM model optimized by PSO can quickly estimate whether the driver is under alcohol influencewith a high accuracy. The model provides a theoretic support for the non-contact alcohol testing, leading to a practical application in the safety assistant driving system. |
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| Keywords: | driving behaviors driving under alcohol influence particle swarm optimization(PSO) support vector machine(SVM) |
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